1. Abstract: Microfinance institutions (MFI) currently serve the financial services needs of 100 million people who live in developing countries. Many of these individuals live in communities with no access to traditional banking services such as loans and savings accounts and must rely on MFIs for these services. With an estimated 500 million people who can utilize MFIs there is an estimated $200 - $250 billion funding gap. The chief obstacle to bridging the funding gap is the low profitability of MFIs. “Microfinance Support System” proposes the ground work for a software package that will aid MFIs around the world in making better decisions with regard to their loan portfolios to increase profitability. The system provides a unique service not found in the current offerings of commercial business intelligence tools and seeks to provide actionable decision analysis and metrics to large multinational MFIs. The objective is to provide better information synthesis to MFI’s to increase profitability and consequently attract additional investments. The system consists of three components. First is a commodity pricing model. To date this model has been able to predict prices six months forward with a range of 80% - 89% accuracy. Second is a cash flow prediction model. Using the predicted commodity price, the cash flow model outputs a suggested loan payback schedule for loan candidates based on their business characteristics. Third is an ideal- basket model. By applying modern portfolio theory, the model outputs investment weights for loan portfolios to minimize risk while maximizing returns. Together these three models form a powerful toolbox which MFIs can rely upon to make better informed decisions. Authors: Edward Chan ESE’12 Sudeep De ESE’12 Bryan Hsu ESE’12 Joon Kim ESE’12 Team #12 Advisor: Dr. Keith Weigelt Microfinance Support System Microfinance Overview Microfinance describes the process of allocating financial resources to entrepreneurs and small business that do not have access to traditional banking sources. Microfinance Institutions seek to serve these entrepreneurs. By empowering these underserved with access to capital, these individuals can build businesses or improve existing ones and earn more income which they can then reinvest in their communities, thereby uplifting other local businesses and ultimately the entire community. Form groupLiving below poverty line 5 Days Complete training/start saving $ Obtain loan/open or expand business Receive mentoring and peer support Repay Loan 6-12 months New Loan Grow business Microfinance Support System Overview Macroeconomic Data Climatological Data Microeconomic Data Historical Pricing Data Geographical Data Raw Data Geographical Focus Investment Criteria Targeted Business Type FilterMFI Intelligence Tool Commodities Pricing Model Debt Cash Flow Model Ideal Basket Model Cash Flow Predictions Debt Portfolio Allocation Output Recommendations for money allocation The Microfinance Intelligence Toolkit consist of three main subsystems: 1.The Commodities Pricing Model 2.The Debt Cash Flow Model 3.The Ideal Basket Model The toolkit will be utilized directly by the MFI’s in determining loan size and feasibility for their clients. By leveraging public data and proprietary MFI information, the toolkit will be able to analyze a loan opportunity from two main perspectives: Cash Flow Prediction – Construction of a debt repayment schedule based on the business factors affecting the loan client and their relative riskiness. Debt Portfolio Allocation – By applying modern portfolio investment theory to the loan portfolios of MFIs, the model seeks fund allocation weights within user selected industries to maximize return for a given risk appetite. Commodities Pricing Model (CPM)Debt Cash Flow Model (DCFM)Ideal Basket Model (IBM) User Interface ExperienceStakeholder Validation Purpose: To predict the price of a certain significant commodity six months in the future. Value Added: MFI’s invest heavily in agriculture which is intrinsically influenced by commodities prices. By predicting future prices, MFI’s will be able to better forecast the income of many their clients who are affected by prices of various commodities. Corn Prices 6 mo. Forward vs. Actual Price Purpose: To model a debt repayment schedule for individual loans based on the business factors affecting clients. Value Added: By meshing the commodities pricing predictions with the cash flow model, MFI’s will have the ability to predict their loan cash flows. They will also be able to adjust cash flow predictions should certain factors such as the price of corn rise dramatically during the duration of the loan. Selected Inputs Available within the Model Purpose: To adeptly apply portfolio diversification theory to allocate MFI funds across user selected industries. Value Added: Use of modern portfolio theory in this regard has never before been utilized in a setting like this for MFIs. By determining the optimal allocation of available funds to select investment areas, MFIs will be able to maximize returns for a defined risk appetite. Efficient Set of Loan Portfolios with All Industries With a plethora of tools the user has access to a wide range of information to use in their decision making process. This guide will walk through a specific scenario following a wheat farmer’s interaction with the MFI. The MFI will input wheat to determine the price of the commodity 6 mo. forward using the CPM. The price will be used to predict potential revenue. Using the IBM the MFI will assess the appeal of investing in the farmer and provide an accurate risk – return profile for the MFI. The MFI will input assumptions into the DCFM to judge the financial health of the borrower and terms of the loan can be adjusted accordingly. 123 Microfinance Support System is an breakthrough project that will begin to push the Microfinance Industry to profitability and help bridge the massive funding gap that currently exists. -Tony Singleton Former Head of Africa Banking, FINCA This intelligence platform will turn a purely philanthropic investment into an attractive asset that every retail investor will want in their future portfolios. -Michael Weinberg Financial Analyst, AllianceBernstein